Computer tools to pinpoint harmful mutations in cancer
Computational methods to decipher somatic alterations in cancer genomes
Building AI-based tools to tell which mutations in a person's tumor are likely driving their cancer so clinicians can better target treatment.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Princeton University NIH-funded |
| Lab location | 1 site (Princeton, UNITED STATES) |
| Project ID | NIH-11225118 on NIH RePORTER |
What this research studies
This project develops machine-learning algorithms that analyze DNA changes in human tumors to predict which specific mutations help cancer grow or affect immune responses. The team combines detailed protein-level features and cellular network information to score and interpret individual mutations. They will also create software that visualizes these predictions and shows how mutations may alter downstream genes and pathways. The work uses existing cancer genome data and aims to produce tools that clinicians and researchers can apply to patient sequencing results.
Who could benefit from this research
Good fit: People with cancer who have undergone tumor genomic sequencing or who can provide tumor tissue or sequencing data to researchers.
Not a fit: Patients without tumor genomic data or whose cancers are driven mainly by non-genetic factors are unlikely to benefit directly from these tools.
Why it matters
Potential benefit: If successful, these tools could help identify actionable mutations and support more personalized treatment and immunotherapy choices for patients.
How similar studies have performed: Related computational methods have helped uncover driver genes and guide research, but reliably predicting the impact of individual mutations for clinical decisions remains challenging and partly unproven.
Where this research is happening
Princeton, UNITED STATES
- Princeton University — Princeton, United States (Active)
Researchers
- Principal investigator: Singh, Mona — Princeton University
- Study coordinator: Singh, Mona
About this research
- This is an active NIH-funded research project — typically early-stage science, not a clinical trial accepting patient enrollment.
- Some NIH-funded labs run parallel clinical studies or seek volunteers for related work. To check, contact the principal investigator or institution listed above.
- For full project details, budget, and progress reports, visit the official NIH RePORTER page below.